mnist.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/LauraVP1994/Machine-Learning-Course-2days/blob/main/mnist.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "LNUgDFfHCN9C"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "41-QVpqtCN9E"
      },
      "source": [
        "# MNIST digit classification: Classical ML\n",
        "\n",
        "## 1. The dataset\n",
        "\n",
        "The MNIST dataset<sup>1</sup> (Modified National Institute of Standards and Technology dataset) is a large dataset containing pre-processed **28x28 pixel** images of handwritten digits. The dataset is widely used for training and testing in the field of machine learning.\n",
        "\n",
        "<sub>[1] THE MNIST DATABASE of handwritten digits, Yann LeCun, Courant Institute, NYU Corinna Cortes, Google Labs, New York Christopher J.C. Burges, Microsoft Research, Redmond.</sub>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nWEl69YjCN9H"
      },
      "source": [
        "Let's load this dataset. As this is a widely used dataset in Machine Learning, it can be loaded straight from the [openml.org](https://www.openml.org/) public repository with the following Scikit-learn function: "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "ZGJnZj0fLtCU"
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import fetch_openml\n",
        "\n",
        "mnist = fetch_openml('mnist_784', as_frame=False, cache=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UySLb5ZeCN9J"
      },
      "source": [
        "The `fetch_openml()` function returns a Python [dictionary-like object](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_openml.html). The actual data can be obtained with the following keys:\n",
        "\n",
        "- data: np.array, scipy.sparse.csr_matrix of floats, or pandas DataFrame\n",
        "- target: np.array, pandas Series or DataFrame\n",
        "\n",
        "Let's explore the feature vectors:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "OZajNV0_CN9J",
        "outputId": "9fa4dd88-f983-4562-95f8-8a5927050948",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Length of feature vector: 784\n",
            "\n",
            "Example of feature vector:\n",
            "\n",
            "[  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   3.  18.\n",
            "  18.  18. 126. 136. 175.  26. 166. 255. 247. 127.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.  30.  36.  94. 154. 170. 253.\n",
            " 253. 253. 253. 253. 225. 172. 253. 242. 195.  64.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.  49. 238. 253. 253. 253. 253. 253.\n",
            " 253. 253. 253. 251.  93.  82.  82.  56.  39.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.  18. 219. 253. 253. 253. 253. 253.\n",
            " 198. 182. 247. 241.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.  80. 156. 107. 253. 253. 205.\n",
            "  11.   0.  43. 154.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.  14.   1. 154. 253.  90.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0. 139. 253. 190.\n",
            "   2.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.  11. 190. 253.\n",
            "  70.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.  35. 241.\n",
            " 225. 160. 108.   1.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.  81.\n",
            " 240. 253. 253. 119.  25.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "  45. 186. 253. 253. 150.  27.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.  16.  93. 252. 253. 187.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0. 249. 253. 249.  64.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "  46. 130. 183. 253. 253. 207.   2.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.  39. 148.\n",
            " 229. 253. 253. 253. 250. 182.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.  24. 114. 221. 253.\n",
            " 253. 253. 253. 201.  78.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.  23.  66. 213. 253. 253. 253.\n",
            " 253. 198.  81.   2.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.  18. 171. 219. 253. 253. 253. 253. 195.\n",
            "  80.   9.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.  55. 172. 226. 253. 253. 253. 253. 244. 133.  11.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0. 136. 253. 253. 253. 212. 135. 132.  16.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
            "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n"
          ]
        }
      ],
      "source": [
        "print(\"Length of feature vector: {}\\n\".format(len(mnist.data[0])))\n",
        "print(\"Example of feature vector:\\n\")\n",
        "print(mnist.data[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "78boMgKaCN9K"
      },
      "source": [
        "Each image in the loaded dataset is represented by a 784 dimensional vector with one gray-scale value (0 means black, 255 means white) for each of the 28x28 pixels.\n",
        "\n",
        "We can reshape this feature vector to the gray-scaled image as follows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "9EvyOYNcCN9L",
        "outputId": "df1268ee-5188-477a-b778-b87a8ae33d3c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
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            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "mnist.data[0].reshape((28,28))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7e5TjIDpCN9L"
      },
      "source": [
        "We will denote the feature vectors as `X` and the corresponding labels as `y`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "qk6672_eCN9M",
        "outputId": "4ef3c6b6-7a0d-4ce9-ab10-34579d81ecee",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "float64\n",
            "object\n"
          ]
        }
      ],
      "source": [
        "X = mnist.data\n",
        "y = mnist.target\n",
        "\n",
        "print(X.dtype)\n",
        "print(y.dtype)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dNRhftMXCN9N"
      },
      "source": [
        "Notice that the labels are Python objects (strings):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "Tn8s9o7bCN9O",
        "outputId": "4f88bfe5-a7bd-44a7-c49e-a5c724c2ea35",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['5' '0' '4' ... '4' '5' '6']\n"
          ]
        }
      ],
      "source": [
        "print(y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OkgQIo4bCN9O"
      },
      "source": [
        "We convert these to numbers (integers):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "WhP5eKg_CN9P"
      },
      "outputs": [],
      "source": [
        "y = mnist.target.astype('int64')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eupOYSl8CN9P"
      },
      "source": [
        "We can use the Python [matplotlib](https://matplotlib.org/) library to plot the digit images in `X` (the label for each image is shown in the title):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "wk9P2m_oMTKe",
        "outputId": "a2dbbe4d-36b8-4fbf-9da1-c4878535a2d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from random import randint\n",
        "\n",
        "# Display 9 randomly selected images\n",
        "for c in range(1, 10):\n",
        "    plt.subplot(3, 3,c)\n",
        "    i = randint(0,X.shape[0])\n",
        "    im = X[i].reshape((28,28))\n",
        "    plt.axis(\"off\")\n",
        "    plt.title(\"Label = {}\".format(y[i]))\n",
        "    plt.imshow(im, cmap='gray')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wl8qUpQtCN9Q"
      },
      "source": [
        "It is 'best practice' in Machine Learning to normalize the feature values such that all features have values with the same 'not to large' scale. This facilitates faster convergence during training.\n",
        "\n",
        "Scikit-learn has functions to normalize features in `sklearn.preprocessing`. The most common ones are [`MinMaxScaler()`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html) and [`StandardScaler()`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html). \n",
        "\n",
        "We will use the `MinMaxScaler()`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "12sdoLpZCN9Q",
        "outputId": "ba3381fa-cf8d-4fa7-8169-36e2d67cbba4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.01176471 0.07058824 0.07058824 0.07058824\n",
            " 0.49411765 0.53333333 0.68627451 0.10196078 0.65098039 1.\n",
            " 0.96862745 0.49803922 0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.11764706 0.14117647 0.36862745 0.60392157\n",
            " 0.66666667 0.99215686 0.99215686 0.99215686 0.99215686 0.99215686\n",
            " 0.88235294 0.6745098  0.99215686 0.94901961 0.76470588 0.25098039\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.19215686\n",
            " 0.93333333 0.99215686 0.99215686 0.99215686 0.99215686 0.99215686\n",
            " 0.99215686 0.99215686 0.99215686 0.98431373 0.36470588 0.32156863\n",
            " 0.32156863 0.21960784 0.15294118 0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.07058824 0.85882353 0.99215686\n",
            " 0.99215686 0.99215686 0.99215686 0.99215686 0.77647059 0.71372549\n",
            " 0.96862745 0.94509804 0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.31372549 0.61176471 0.41960784 0.99215686\n",
            " 0.99215686 0.80392157 0.04313725 0.         0.16862745 0.60392157\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.05490196 0.00392157 0.60392157 0.99215686 0.35294118\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.54509804 0.99215686 0.74509804 0.00784314 0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.04313725\n",
            " 0.74509804 0.99215686 0.2745098  0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.1372549  0.94509804\n",
            " 0.88235294 0.62745098 0.42352941 0.00392157 0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.31764706 0.94117647 0.99215686\n",
            " 0.99215686 0.46666667 0.09803922 0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.17647059 0.72941176 0.99215686 0.99215686\n",
            " 0.58823529 0.10588235 0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.0627451  0.36470588 0.98823529 0.99215686 0.73333333\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.97647059 0.99215686 0.97647059 0.25098039 0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.18039216 0.50980392 0.71764706 0.99215686\n",
            " 0.99215686 0.81176471 0.00784314 0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.15294118 0.58039216\n",
            " 0.89803922 0.99215686 0.99215686 0.99215686 0.98039216 0.71372549\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.09411765 0.44705882 0.86666667 0.99215686 0.99215686 0.99215686\n",
            " 0.99215686 0.78823529 0.30588235 0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.09019608 0.25882353 0.83529412 0.99215686\n",
            " 0.99215686 0.99215686 0.99215686 0.77647059 0.31764706 0.00784314\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.07058824 0.67058824\n",
            " 0.85882353 0.99215686 0.99215686 0.99215686 0.99215686 0.76470588\n",
            " 0.31372549 0.03529412 0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.21568627 0.6745098  0.88627451 0.99215686 0.99215686 0.99215686\n",
            " 0.99215686 0.95686275 0.52156863 0.04313725 0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.53333333 0.99215686\n",
            " 0.99215686 0.99215686 0.83137255 0.52941176 0.51764706 0.0627451\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.        ]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "X = MinMaxScaler().fit_transform(X)\n",
        "\n",
        "print(X[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FpTEDYnICN9R"
      },
      "source": [
        "To evaluate our trained model we need to first create an independent test set with images that are not used during training:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "XQuFEed-McB8",
        "outputId": "00abcc8e-e6ad-4eea-9e7c-39e881868264",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(66500, 784)\n",
            "(3500, 784)\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05, shuffle=True, random_state=42)\n",
        "\n",
        "print(X_train.shape)\n",
        "print(X_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t2PANJXvCN9S"
      },
      "source": [
        "## 2. The model\n",
        "\n",
        "Now we are ready to define our model. We will fit a [Logistic Regression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "ex08esqVOSYv"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "clf = LogisticRegression()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jQO0xVmRCN9T"
      },
      "source": [
        "Each Scikit-learn model has a function `fit()` that optimizes the modelparameters to minimize the cost function:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "utVxm0K7CN9U",
        "outputId": "fe9b09db-d16a-44ff-9928-0e0a342a4043",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "clf.fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2rLDUbMpCN9U"
      },
      "source": [
        "We can access the modelparameters of the fitted logistic regression model as follows: "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "C23ngOMkCN9V",
        "outputId": "2d21e725-733a-4170-e52f-2ead6a32ce7b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " ...\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]]\n",
            "[-1.0398946   1.19590291  0.00848795 -0.71122705  0.22600473  1.95644647\n",
            " -0.38788602  1.55821525 -2.25246635 -0.55358329]\n"
          ]
        }
      ],
      "source": [
        "print(clf.coef_)\n",
        "print(clf.intercept_)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lx3ygFWsCN9V"
      },
      "source": [
        "As the Scikit-learn implementation of logistic regression performs a one-vs-all multi-class apporach we get 10 lists of modelparameters and 10 intercepts, one for each of the 10 classes.\n",
        "\n",
        "Let's plot the modelparameters for each class as a 28x28 image:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "_xZy-MP6PlnT",
        "outputId": "2aa84e8a-87bf-418b-f5ee-75ada74d290a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x432 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "coef = clf.coef_\n",
        "scale = np.abs(coef).max()\n",
        "plt.figure(figsize=(16,6))\n",
        "\n",
        "for i in range(10): # 0-9\n",
        "    coef_plot = plt.subplot(2, 5, i + 1) # 2x5 plot\n",
        "\n",
        "    coef_plot.imshow(coef[i].reshape(28,28), \n",
        "                     cmap=plt.cm.RdBu,\n",
        "                     vmin=-scale, vmax=scale,\n",
        "                    interpolation='bilinear')\n",
        "    \n",
        "    coef_plot.set_xticks(()); coef_plot.set_yticks(()) # remove ticks\n",
        "    coef_plot.set_xlabel(f'Class {i}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cm35GlpvCN9W"
      },
      "source": [
        "Each Scikit-learn model also has a function `predict()` that applies the fitted model to compute class lables for feature vectors:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "8Kr2uTG6CN9W",
        "outputId": "a12999bc-538e-422b-c7b7-c8cf58c91dc7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[8 4 5 ... 7 7 2]\n"
          ]
        }
      ],
      "source": [
        "# Perform the predictions\n",
        "y_predicted = clf.predict(X_test)\n",
        "\n",
        "print(y_predicted)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a5M754QuCN9W"
      },
      "source": [
        "## 3. Evaluation\n",
        "\n",
        "Scikit-learn offers many [metrics](https://scikit-learn.org/stable/modules/model_evaluation.html) for evaluating the prediction performance. The most common metric is `accuracy`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "Sk5OTSWvCN9W",
        "outputId": "5c77aad5-6858-4c7d-b07a-e686a820ffde",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy = 91.94285714285715%\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "print(\"Accuracy = {}%\".format(accuracy_score(y_test, y_predicted)*100))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hfhXWlYvCN9X"
      },
      "source": [
        "To get more insight into the prediction errors for each class we can compute a confusion matrix:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "uB9LvM1eO3dX",
        "outputId": "34b26f54-4d71-4e46-9bba-f8473989d9a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "\n",
        "ConfusionMatrixDisplay.from_predictions(y_test, y_predicted)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pB8TrWqQCN9Y"
      },
      "source": [
        "Scikit-learn also offers a `classification_report()` function that computes metrics that are more suitable for imbalanced multi-class classification tasks:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "ZRy-UUrjOvki",
        "outputId": "ddfce2fe-a79e-4bda-df4c-b79e47f26f0c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.96      0.97       346\n",
            "           1       0.95      0.98      0.96       378\n",
            "           2       0.87      0.89      0.88       332\n",
            "           3       0.90      0.90      0.90       400\n",
            "           4       0.92      0.93      0.92       295\n",
            "           5       0.88      0.90      0.89       335\n",
            "           6       0.94      0.94      0.94       351\n",
            "           7       0.92      0.93      0.92       362\n",
            "           8       0.90      0.86      0.88       348\n",
            "           9       0.94      0.91      0.92       353\n",
            "\n",
            "    accuracy                           0.92      3500\n",
            "   macro avg       0.92      0.92      0.92      3500\n",
            "weighted avg       0.92      0.92      0.92      3500\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "print(classification_report(y_test, y_predicted, labels=range(0,10)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zJvaMTC2CN9Y"
      },
      "source": [
        "Now, let's take a look a the misclassified images in the test set: "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "bPKWegNvQXvg",
        "outputId": "d8e5f21d-b47c-4bb1-e50a-76b265090ccb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of misclassified test set images: 282\n"
          ]
        }
      ],
      "source": [
        "index = 0\n",
        "misclassified_images = []\n",
        "for label, predict in zip(y_test, y_predicted):\n",
        "    if label != predict: \n",
        "        misclassified_images.append(index)\n",
        "    index +=1\n",
        "    \n",
        "print(\"Number of misclassified test set images: {}\".format(len(misclassified_images)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RmFlNCXVUZy5",
        "outputId": "68b7c420-ceb9-45a6-c97b-97ae36ef75e7"
      },
      "source": [
        "Let's plot some of these:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "D4BHrTiEUcMJ",
        "outputId": "4158ba06-4b1b-4715-a776-f98472713ab4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 572
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 20 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "for plot_index, bad_index in enumerate(misclassified_images[0:20]):\n",
        "    p = plt.subplot(4,5, plot_index+1) # 4x5 plot\n",
        "    \n",
        "    p.imshow(X_test[bad_index].reshape(28,28), cmap=plt.cm.gray,\n",
        "            interpolation='bilinear')\n",
        "    p.set_xticks(()); p.set_yticks(()) # remove ticks\n",
        "    \n",
        "    p.set_title(f'Pred: {y_predicted[bad_index]}, Actual: {y_test[bad_index]}');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M4sqfqAqCN9Z"
      },
      "source": [
        "## 4. Hyperparameter optimization\n",
        "\n",
        "Scikit-learn offers [many functions](https://scikit-learn.org/stable/modules/grid_search.html) for hyperparameter optimization. We will use `GridSearchCV()` that evaluates different hyperparamter value combinations using cross-validation.\n",
        "\n",
        "In `GridSearchCV()` you define the hyperparameter values to consider as a Python dictionary:  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "Va9mCdapCN9a",
        "outputId": "6aee2209-2522-4264-94fe-5085de20a59a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'C': array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03])}\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "grid = {\n",
        "    \"C\":np.logspace(-3,3,7)\n",
        "}\n",
        "\n",
        "print(grid)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BLpwxC2CCN9a"
      },
      "source": [
        "Next, we can initialize `GridSearchCV()` just like any other model in Scikit-learn:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "wbD1n_jWCN9a"
      },
      "outputs": [],
      "source": [
        "clf_cv = GridSearchCV(clf, grid, cv=5, verbose=2, scoring='f1_macro')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VIldBidvCN9b"
      },
      "source": [
        "Now we can just call the `fit()` function again to fit all the models and evaluate their prediction performance with the cross-validation procedure:"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "idx = np.random.choice(len(X_train),2000,replace=False)\n",
        "X_train_small = X_train[idx]\n",
        "y_train_small = y_train[idx]"
      ],
      "metadata": {
        "id": "-CT3-4zvDMAe"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "_NRPKsODcrV2",
        "outputId": "e7894cab-73e6-4c81-ff6f-bdb9c81dd2dc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 7 candidates, totalling 35 fits\n",
            "[CV] END ............................................C=0.001; total time=   0.4s\n",
            "[CV] END ............................................C=0.001; total time=   0.3s\n",
            "[CV] END ............................................C=0.001; total time=   0.4s\n",
            "[CV] END ............................................C=0.001; total time=   0.4s\n",
            "[CV] END ............................................C=0.001; total time=   0.4s\n",
            "[CV] END .............................................C=0.01; total time=   0.9s\n",
            "[CV] END .............................................C=0.01; total time=   0.9s\n",
            "[CV] END .............................................C=0.01; total time=   0.8s\n",
            "[CV] END .............................................C=0.01; total time=   0.9s\n",
            "[CV] END .............................................C=0.01; total time=   0.8s\n",
            "[CV] END ..............................................C=0.1; total time=   0.9s\n",
            "[CV] END ..............................................C=0.1; total time=   0.9s\n",
            "[CV] END ..............................................C=0.1; total time=   0.9s\n",
            "[CV] END ..............................................C=0.1; total time=   1.0s\n",
            "[CV] END ..............................................C=0.1; total time=   0.9s\n",
            "[CV] END ..............................................C=1.0; total time=   0.9s\n",
            "[CV] END ..............................................C=1.0; total time=   0.9s\n",
            "[CV] END ..............................................C=1.0; total time=   0.9s\n",
            "[CV] END ..............................................C=1.0; total time=   0.9s\n",
            "[CV] END ..............................................C=1.0; total time=   0.9s\n",
            "[CV] END .............................................C=10.0; total time=   0.9s\n",
            "[CV] END .............................................C=10.0; total time=   0.9s\n",
            "[CV] END .............................................C=10.0; total time=   0.9s\n",
            "[CV] END .............................................C=10.0; total time=   0.9s\n",
            "[CV] END .............................................C=10.0; total time=   0.9s\n",
            "[CV] END ............................................C=100.0; total time=   1.0s\n",
            "[CV] END ............................................C=100.0; total time=   0.9s\n",
            "[CV] END ............................................C=100.0; total time=   1.0s\n",
            "[CV] END ............................................C=100.0; total time=   0.9s\n",
            "[CV] END ............................................C=100.0; total time=   0.9s\n",
            "[CV] END ...........................................C=1000.0; total time=   0.9s\n",
            "[CV] END ...........................................C=1000.0; total time=   0.9s\n",
            "[CV] END ...........................................C=1000.0; total time=   0.9s\n",
            "[CV] END ...........................................C=1000.0; total time=   0.9s\n",
            "[CV] END ...........................................C=1000.0; total time=   0.9s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
              "             param_grid={'C': array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03])},\n",
              "             scoring='f1_macro', verbose=2)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "clf_cv.fit(X_train_small,y_train_small)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VnX2blc4CN9c"
      },
      "source": [
        "A fitted `GridSearchCV()` has an attribute `cv_results_` that contains the cross-validation scores for each of the hyperparameter value combinations considered. The following code creates a Pandas Dataframe from `cv_results_` for easy visualization:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "W8iCagOKdD79",
        "outputId": "49b2f850-2c5f-4532-d413-fca70bbb2126",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  param_C     score\n",
              "0   0.001  0.777229\n",
              "1    0.01  0.858924\n",
              "2     0.1  0.887514\n",
              "3     1.0  0.875130\n",
              "4    10.0  0.869170\n",
              "5   100.0  0.867929\n",
              "6  1000.0  0.865210"
            ],
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              "      <th></th>\n",
              "      <th>param_C</th>\n",
              "      <th>score</th>\n",
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              "      <td>0.887514</td>\n",
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              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
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              "      <th>6</th>\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "result_cv = pd.DataFrame()\n",
        "result_cv[\"param_C\"] = clf_cv.cv_results_[\"param_C\"].data\n",
        "result_cv[\"score\"] = clf_cv.cv_results_[\"mean_test_score\"]\n",
        "\n",
        "result_cv"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1zBiGNqvCN9c"
      },
      "source": [
        "A fitted `GridSearchCV()` also has attributes `best_estimator_` and `best_score_` that contain the best performing model and its corresponding cross-validation score respectively: "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "7sG_9VstCN9c",
        "outputId": "9d53d599-af41-43f2-c3a3-f3396ab1e999",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LogisticRegression(C=0.1)\n",
            "0.8875135066686031\n"
          ]
        }
      ],
      "source": [
        "print(clf_cv.best_estimator_)\n",
        "print(clf_cv.best_score_)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i1srM1rjlav7"
      },
      "source": [
        "A fitted `GridSearchCV()` also has the function `predict()` that applies `best_estimator_` to predict the classes:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "5-HwnG9DCN9e",
        "outputId": "b3388d4b-97e2-4b3b-ee34-af1d5b0779ff",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.94      0.94       346\n",
            "           1       0.87      0.98      0.92       378\n",
            "           2       0.83      0.84      0.84       332\n",
            "           3       0.90      0.84      0.87       400\n",
            "           4       0.87      0.91      0.89       295\n",
            "           5       0.84      0.86      0.85       335\n",
            "           6       0.92      0.92      0.92       351\n",
            "           7       0.90      0.89      0.90       362\n",
            "           8       0.88      0.80      0.84       348\n",
            "           9       0.87      0.86      0.86       353\n",
            "\n",
            "    accuracy                           0.88      3500\n",
            "   macro avg       0.88      0.88      0.88      3500\n",
            "weighted avg       0.88      0.88      0.88      3500\n",
            "\n"
          ]
        }
      ],
      "source": [
        "y_predicted = clf_cv.predict(X_test)\n",
        "\n",
        "print(classification_report(y_test, y_predicted, labels=range(0,10)))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf = LogisticRegression(C=0.1)\n",
        "\n",
        "clf.fit(X_train,y_train)\n",
        "\n",
        "y_predicted = clf.predict(X_test)\n",
        "\n",
        "print(classification_report(y_test, y_predicted, labels=range(0,10)))"
      ],
      "metadata": {
        "id": "nbf8WrWOEedX",
        "outputId": "8fc48316-784e-4a8d-9f01-91e732f30534",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.96      0.97       346\n",
            "           1       0.94      0.98      0.96       378\n",
            "           2       0.86      0.90      0.88       332\n",
            "           3       0.91      0.88      0.89       400\n",
            "           4       0.92      0.94      0.93       295\n",
            "           5       0.88      0.89      0.89       335\n",
            "           6       0.94      0.94      0.94       351\n",
            "           7       0.92      0.93      0.92       362\n",
            "           8       0.90      0.87      0.89       348\n",
            "           9       0.94      0.90      0.92       353\n",
            "\n",
            "    accuracy                           0.92      3500\n",
            "   macro avg       0.92      0.92      0.92      3500\n",
            "weighted avg       0.92      0.92      0.92      3500\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X3rSeI-mCN9e"
      },
      "source": [
        "The `predict()` function returns the classes only. The logisitic regression algorithm 'predicts' probabilities for each class. The `predict_proba()` functions returns these probabilities:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "oS1HWw8cCN9e"
      },
      "outputs": [],
      "source": [
        "y_predicted = clf.predict_proba(X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RzlsKn-5CN9e"
      },
      "source": [
        "These are the class probability predictions for the first instance in the test set `y_test`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "_28YFYMLCN9f",
        "outputId": "3ca031b9-846d-4c19-9e67-47b468fee125",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1.14200186e-08 2.28488770e-10 6.77989438e-06 1.19780627e-04\n",
            " 9.06515186e-09 6.79122936e-04 2.86283263e-10 7.38096005e-10\n",
            " 9.99193676e-01 6.19300332e-07]\n"
          ]
        }
      ],
      "source": [
        "print(y_predicted[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JorMjSraCN9f"
      },
      "source": [
        "This is the true class of this instance:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "2hWpNGZjCN9f",
        "outputId": "cf9d8840-d42c-4823-ea1d-ab2c5eefb0f7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8\n"
          ]
        }
      ],
      "source": [
        "print(y_test[0])"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "mnist.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
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        "version": 3
      },
      "file_extension": ".py",
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